17,390 research outputs found
Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation
Imitation learning is an effective approach for autonomous systems to acquire
control policies when an explicit reward function is unavailable, using
supervision provided as demonstrations from an expert, typically a human
operator. However, standard imitation learning methods assume that the agent
receives examples of observation-action tuples that could be provided, for
instance, to a supervised learning algorithm. This stands in contrast to how
humans and animals imitate: we observe another person performing some behavior
and then figure out which actions will realize that behavior, compensating for
changes in viewpoint, surroundings, object positions and types, and other
factors. We term this kind of imitation learning "imitation-from-observation,"
and propose an imitation learning method based on video prediction with context
translation and deep reinforcement learning. This lifts the assumption in
imitation learning that the demonstration should consist of observations in the
same environment configuration, and enables a variety of interesting
applications, including learning robotic skills that involve tool use simply by
observing videos of human tool use. Our experimental results show the
effectiveness of our approach in learning a wide range of real-world robotic
tasks modeled after common household chores from videos of a human
demonstrator, including sweeping, ladling almonds, pushing objects as well as a
number of tasks in simulation.Comment: Accepted at ICRA 2018, Brisbane. YuXuan Liu and Abhishek Gupta had
equal contributio
Time-Contrastive Networks: Self-Supervised Learning from Video
We propose a self-supervised approach for learning representations and
robotic behaviors entirely from unlabeled videos recorded from multiple
viewpoints, and study how this representation can be used in two robotic
imitation settings: imitating object interactions from videos of humans, and
imitating human poses. Imitation of human behavior requires a
viewpoint-invariant representation that captures the relationships between
end-effectors (hands or robot grippers) and the environment, object attributes,
and body pose. We train our representations using a metric learning loss, where
multiple simultaneous viewpoints of the same observation are attracted in the
embedding space, while being repelled from temporal neighbors which are often
visually similar but functionally different. In other words, the model
simultaneously learns to recognize what is common between different-looking
images, and what is different between similar-looking images. This signal
causes our model to discover attributes that do not change across viewpoint,
but do change across time, while ignoring nuisance variables such as
occlusions, motion blur, lighting and background. We demonstrate that this
representation can be used by a robot to directly mimic human poses without an
explicit correspondence, and that it can be used as a reward function within a
reinforcement learning algorithm. While representations are learned from an
unlabeled collection of task-related videos, robot behaviors such as pouring
are learned by watching a single 3rd-person demonstration by a human. Reward
functions obtained by following the human demonstrations under the learned
representation enable efficient reinforcement learning that is practical for
real-world robotic systems. Video results, open-source code and dataset are
available at https://sermanet.github.io/imitat
Flight Dynamics-based Recovery of a UAV Trajectory using Ground Cameras
We propose a new method to estimate the 6-dof trajectory of a flying object
such as a quadrotor UAV within a 3D airspace monitored using multiple fixed
ground cameras. It is based on a new structure from motion formulation for the
3D reconstruction of a single moving point with known motion dynamics. Our main
contribution is a new bundle adjustment procedure which in addition to
optimizing the camera poses, regularizes the point trajectory using a prior
based on motion dynamics (or specifically flight dynamics). Furthermore, we can
infer the underlying control input sent to the UAV's autopilot that determined
its flight trajectory.
Our method requires neither perfect single-view tracking nor appearance
matching across views. For robustness, we allow the tracker to generate
multiple detections per frame in each video. The true detections and the data
association across videos is estimated using robust multi-view triangulation
and subsequently refined during our bundle adjustment procedure. Quantitative
evaluation on simulated data and experiments on real videos from indoor and
outdoor scenes demonstrates the effectiveness of our method
Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots
In the last decade, many medical companies and research groups have tried to
convert passive capsule endoscopes as an emerging and minimally invasive
diagnostic technology into actively steerable endoscopic capsule robots which
will provide more intuitive disease detection, targeted drug delivery and
biopsy-like operations in the gastrointestinal(GI) tract. In this study, we
introduce a fully unsupervised, real-time odometry and depth learner for
monocular endoscopic capsule robots. We establish the supervision by warping
view sequences and assigning the re-projection minimization to the loss
function, which we adopt in multi-view pose estimation and single-view depth
estimation network. Detailed quantitative and qualitative analyses of the
proposed framework performed on non-rigidly deformable ex-vivo porcine stomach
datasets proves the effectiveness of the method in terms of motion estimation
and depth recovery.Comment: submitted to IROS 201
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